Any guidance would be greatly appreciated for this question.
For my data I am looking at two species that have very low detection probabilities, with 10 repeated surveys in 20 separate study units. I've run single-season analysis with covariates. When modeling the covariates as one per model some models generate individual site estimates and other models generate the following:
Individual Site estimates of <Psi>
Site Survey Psi Std.err 95% conf. interval
1 site 1 1-1: 1.0000 0.0000 1.0000 - 1.0000
2 site 2 1-1: 1.0000 0.0000 1.0000 - 1.0000
3 site 3 1-1: 1.0000 -1.#IND 1.#QNB - 1.#QNB
4 site 4 1-1: 1.0000 -1.#IND 1.#QNB - 1.#QNB
5 site 5 1-1: 1.0000 -1.#IND 1.#QNB - 1.#QNB
6 site 6 1-1: 1.0000 -1.#IND 1.#QNB - 1.#QNB
7 site 7 1-1: 1.0000 -1.#IND 1.#QNB - 1.#QNB
8 site 8 1-1: 1.0000 -1.#IND 1.#QNB - 1.#QNB
9 site 9 1-1: 1.0000 0.0000 1.0000 - 1.0000
10 site 10 1-1: 1.0000 0.0000 1.0000 - 1.0000
11 site 11 1-1: 1.0000 -1.#IND 1.#QNB - 1.#QNB
12 site 12 1-1: 1.0000 -1.#IND 1.#QNB - 1.#QNB
13 site 13 1-1: 1.0000 0.0000 0.6410 - 1.0000
14 site 14 1-1: 1.0000 0.0000 1.0000 - 1.0000
15 site 15 1-1: 1.0000 0.0000 1.0000 - 1.0000
16 site 16 1-1: 1.0000 0.0000 1.0000 - 1.0000
17 site 17 1-1: 1.0000 0.0000 1.0000 - 1.0000
18 site 18 1-1: 1.0000 -1.#IND 1.#QNB - 1.#QNB
19 site 19 1-1: 0.0000 0.0000 0.0000 - 0.4645
20 site 20 1-1: 1.0000 -1.#IND 1.#QNB - 1.#QNB
Is this a result of minimal detection histories and low detection probabilies?
How should I handle these models?
Thoughts?